[ijeta-v2i3p8]:priya dubey, mr. saurabh jain

International Journal of Engineering Trends and Applications (IJETA) Volume 2 Issue 3, May-June 2015

ISSN: 2393 - 9516 www.ijetajournal.org Page 52

An Efficient Technique for Energy Consumption in Distributed

Cloud Network Using ANT Technique Priya Dubey [1], Mr. Saurabh Jain [2]

M.Tech Scholar [1], Assistant Professor [2] Department of Computer Science and Engineering

Oriental College of Technology

Bhopal India

ABSTRACT Cloud Computing enables various users to send their data over cloud and also in distrusted manner. But during the transmission

of data over distributed network in cloud, energy consumption is more and hence network becomes unstable and costly. Hence

various techniques are implemented for the energy consumption in cloud network, but the technique implemented so far is not an

efficient and stable technique for the energy consumption as well as execution time. Here an efficient technique is implemented

for the energy consumption as well as for the less execution of data transmission and overload over cloud network using Ant

based shortest path.

Keywords:- Cloud, Security, Multi-Keyword, Cloud Computing.

I. INTRODUCTION

Cloud Computing means a remote server that access

through the internet which helps in business applications

and functionality along with the convention of system

software for respective web application. Cloud computing

concept saves capital that cloud users pay out on annual or

monthly payment. Due to advantage of cloud services, more

and more sensitive information are being centralized into the

cloud servers, such as confidential videos and photos, various

emails, personal health records information, corporation

business data, government documents, etc. So as to privacy

problem, data privacy [1] and data loss will be increase in

certain circumstances. When users outsource their private onto

cloud, the cloud service provider able to monitor the

communication between the users and cloud at will trust or

untrusted. As cloud computing is promising development in

computing concept the confidence increase becomes very

important aspect. There are mainly two parameters which can

help to get better the confidence on the cloud services. One

is to improve efficiency and another for improving security. To

improve the efficiency the keyword search method is enhanced

as it makes available two way communications between cloud

server and the cloud customer. But while deploying security

the burden on cloud server gets increased unexpectedly.

Consequently it is extremely significant to maintain these two

factors so that to improve overall efficiency of the cloud

services [2]. Also the world is of mobile devices, so everyone

wants to use cloud services on their mobile devices and if the

computational cost goes to elevated then it effects into

important resource utilization, which is not appropriate for

mobile devices. So current scenario is having need of a

proficient method is cloud services in the expectations.

Figure 1. Architecture of the Cloud Computing

Cloud is a service which can be accessed from everywhere if

arranged in that way at any path. It causes lots of parties or

persons using it for their purpose. In such case the data

Various parties may contribute to within them on the

cloud server can be secret. In addition every cloud user who

uses cloud services doesnt like to get followed. In such cases it is very important to maintain their privacy [3]. Thus to

maintain their privacy the files and even the search requests are

encrypted as soon as the request is sent to the server. This

encryption may also affect the efficiency of searching

techniques as the search should go on in encrypted manner.

Besides, in cloud computing data owners may allocate their

outsourced data with a number of cloud users, who strength

want to only get back the data files they are paying attention in

cloud server. One of the most fashionable ways to do so is

throughout keyword-based retrieval. It is like better to get the

retrieval outcome with the most significant content of the users

which matches with the ranked in order to fill the users



interest. To develop security exclusive of give up effectiveness,

methods here in [4], [5], give you an idea about that they

sustain top-k single keyword retrieval under different

circumstances. To protect data privacy, confidential data has

to be encrypted before outsourcing, so as to provide end-to-

end data confidentiality assurance in the cloud. Clouds enable

customers to remotely store and access their data by lowering

the cost of hardware ownership while providing robust and fast

services [6]. The importance and necessity of privacy

preserving search techniques are even more pronounced in the

cloud applications. Due to the fact that large companies that

operate the public clouds like Google or Amazon may access

the sensitive data and search patterns, hiding the query and the

retrieved data has great importance in ensuring the privacy and

security of those using cloud services. We aim to achieve an

efficient system where any authorized user can perform a

search on a remote database with multiple keywords, not

including exposing neither the keywords he/she searches for,

nor the pleased of the documents he/she get backs. The main

confront of cloud storage is guaranteeing have power over, and

the essential integrity and confidentiality of all stored cloud

data.

Figure 2. Various Models of Cloud Computing

Benefits of Cloud Computing

Although the infrastructures under the cloud are much more powerful and reliable than personal

computing devices, they are still facing the broad

range of both internal and external threats for data

integrity.

Second, there do exist various motivations for CSP to behave unfaithfully toward the cloud users regarding

their outsourced data status.

In particular, simply downloading all the data for its integrity verification is not a practical solution due to

the expensiveness in I/O and transmission cost across

the network. Besides, it is often insufficient to detect

the data corruption only when accessing the data, as

it does not give users correctness assurance for those

unaccessed data and might be too late to recover the

data loss or damage.

Encryption does not completely solve the problem of protecting data privacy against third-party auditing

but just reduces it to the complex key management

domain. Unauthorized data leakage still remains

possible due to the potential exposure of decryption

keys.

II. LITERATURE REVIEW

2014- A. Q. Lawey et. al. they introduced energy efficient

cloud computing services design framework over IP/WDM

core networks. They analyzed distribution of clouds, impact of

demand, cloud capability, access frequency content popularity

along with no. of switches, routers, servers, storage required

in cloud [7]. They examined cloud content delivery, virtual

machines and storage as a service (StaaS).they developed

mixed integer linear programming (MILP) to enhance services

of cloud content delivery by replicating content into multiple

clouds on which they developed energy efficient cloud content

delivery heuristic DEER-CD. They increased power savings

by migrating content according to access frequency and by

optimizing placement of virtual machines by breaking them

into smaller virtual machines and placing them in proximity.

They termed replicating content into multiple clouds based on

content popularity as OPR scheme. With the help of these

schemes they were able to save 92% and 43% network and

total power savings respectively. They obtained power savings

are by placing Virtual machines using a heuristic (DEER-VM)

developed to copy the model behavior in real time [7].

2014 V. Mathew et. al. identified the operational costs of Internet scale distributes systems(IDS).they proposed a demand

response technique in which pricing signals from smart grid

makes the system to reduce energy usage [8]. The load is

deferred from elastic requests to later time periods reducing

server demand and energy usage. They proposed optimal

offline algorithm and showed that cost savings can be achieved

without increasing in bandwidth cost of IDS .the approach used

by them for elastic requests like background downloads,

software updates etc. does not require continuous services. The

algorithm proposed by them achieved 12% of savings on time

of use electricity pricing. They presented a future plan to move

load to near data centers for energy saving [8].

2013- S. Zaman et. al. analyzed that the cloud computing

resources were provisioned to different virtual machine

instances allocated to users for specific period of time which is

not efficient allocation economically due to fixed price

allocation [9]. They proposed combinatorial auction based

allocation which described that users demand is taken into account while making provisioning decisions and VM

allocation because the existing mechanism do not consider

users demand. They evaluated the mechanism through simulation experiments which improved utilization of

resources of clouds and increased the revenue of cloud



provider. They designed mechanism CA-PROVISION which

effectively captured market demand, provisioned the

computing resources and generated higher revenue as

compared to CA-GREEDY [9].

2013- K. Qazi et. al. observed that virtual machines(VM) rent

computational resources like memory, network bandwidth

etc. to data center owners [10]. They stated that the physical

machine that make up cloud termed as machine farms should

optimally use these resources without being overload at a

point and also minimum machine should continue running.

They observed the pattern to help arrange the VM on physical

machines. They proposed a framework PoWER that predicts

the behavior of cluster and distributes VM in cluster turning

off unused physical machines to save energy. They tested

PoWER on tested cluster and analyzed its performance

resulting in better results compared to FFT based time series

method [10].

2013- G. Lahoti et. al. stated that fine grained energy usage

data can leak customer information and due to use of this data

by online service providers for effectiveness of smart grid

technologies ,the sharing of data is increasing [11]. They

proposed privacy enhanced framework to store, manage and

share such data. The mechanism used by them stated that the

customer can control the usage information showing to service

providers which will be convinced by its authenticity. They

implemented a prototype using Green Button data model.

Their prototype worked allowing redaction of Green Button

data model while sharing data with third party service

provider. The presented data can be used for billing and

accounting purpose as it can be authenticated and verified by

third party using Green Button data model. They planned to

work on the working prototype of the model of demand

response aggregation service [11].

III. PROPOSED WORK

Max-min algorithm

Pseudocode for the Negamax version of the minimax

algorithm (using an evaluation heuristic to terminate at a

given depth) is specified below. .

1. Generate construction graph

2. Set the range of pheromone value to min, max where min>0 3. Set m ants at randomly chosen vertices on the

construction graph

4. Initialize trails to max 5. Ant arbitrary moves on the graph to constructs its

solutions

6. If iteration completed then the pheromone trails

consisting of the best solution will be updated

7. The pheromone trail constructs min,



paths from that node to next node is computed

and updated.

5. After first iteration the value of the value of pheromone is calculated at each node of the

network.

6. More value of pheromone attracts more ants, hence the next packet is send to that particular

nodes where pheromone value is maximum.

If N pkts send from one node to other nodes Compute next node based on Max-min ();

Compute next node based on Rank();

Compute next node based on Fuzzy();

Repeat till N packets send from source to destination

For each N pkt to traverse from nod1-> nod2 If Vpher nod2 == Vpher nod3v && If Rnod2 > Rnod3 && Rnod4



# of

packets

Max-

Min

Rank

based

Fuzzy

Rule

based

Proposed

10 7538 6281 5493 4016

20 8015 7329 6283 5002

30 8519 8015 7012 6183

40 9253 8428 8025 6519

50 10163 9712 8581 7153

60 10582 10126 9273 8129

70 11283 11172 10273 9012

80 12263 11428 11239 10176

90 13527 12439 12103 11193

100 14328 13230 12382 11386

Table 3. Comparison of Computational Time

The figure shown below is the analysis and comparison of

various load balancing techniques on the basis of number

of packets send from cloudlet to data centers. The

proposed methodology is more efficient as compared to

the existing techniques of load balancing.

Figure 3. Computational Time in ms

V. CONCLUSION

The proposed methodology implemented here for the

energy consumption in distributed cloud network using

ant based techniques provides an efficient way of

overloading on all the datacenters using brokers. The

experimental results shows that the proposed

methodology implemented here provides less energy

consumption as compared to the existing technique

implemented for the distributed core networks in cloud

computing.

REFERENCES

[1] Cloud Security Alliance, Top Threats to Cloud Computing, http://www.cloudsecurityalliance.org, 2010.

[2] Kui Ren, Cong Wang and Qian Wang, Toward Secure and Effective Data Utilization in Public

Cloud, IEEE Network, November/December 2012.

[3] Cong Wang, Sherman S.M. Chow, Qian Wang, Kui Ren, and Wenjing Lou, Privacy-Preserving Public Auditing for Secure Cloud Storage, IEEE Transactions on Computers, Vol. 62, No. 2, February

2013.

[4] N. Cao, C. Wang, M. Li, K. Ren, and W. Lou, Privacy-Preserving Multikeyword Ranked Search over Encrypted Cloud Data, Proc.IEEE INFOCOM, 2011.

[5] H. Hu, J. Xu, C. Ren, and B. Choi, Processing Private Queries over Untrusted Data Cloud through

Privacy Homomorphism, Proc. IEEE 27th Intl Conf. Data Eng. (ICDE), 2011.

[6] L. M. Vaquero, L. Rodero-Merino, J. Caceres, and M. Lindner. A break in the clouds: towards a cloud

definition. SIGCOMM Comput. Commun. Rev.,

39:50{55, December 2008.

[7] Ahmed Q. Lawey, Taisir E. H. El-Gorashi, and Jaafar M. H. Elmirghani, Distributed Energy Efficient Clouds Over Core Networks, JOURNAL OF LIGHTWAVE TECHNOLOGY, VOL. 32, NO.

7, APRIL 1, 2014.

[8] Virnal Mathewt, Rarnesh K. Sitararnan t and Prashant Shenoy, Reducing Energy Costs In Internet-Scale Distributed Systems Using Load

Shifting, 978-1-4799-3635-9/14/$31.00 2014. [9] Sharrukh Zaman, A Combinatorial Auction-Based

Mechanism for Dynamic VM Provisioning and

Allocation in Clouds, IEEE TRANSACTIONS ON CLOUD COMPUTING,2013.

[10] Kashifuddin Qazi, Yang Li, and Andrew Sohn, PoWER - Prediction of Workload for Energy

Efficient Relocation of Virtual Machines, ACM 978-1-4503-2428-1/13/10.

[11] Gaurav Lahoti, Customer-centric Energy Usage Data Management and Sharing in Smart Grid

Systems, 2013 ACM 978-1-4503-2492-2/13/11.

[ijeta-v2i3p8]:priya dubey, mr. saurabh jain

Documents